To address the challenges of crowd counting in dense scenes,such as complex backgrounds and scale variations,we propose a weakly supervised crowd counting model for dense scenes,named GLCrowd,which integrates global and local attention mechanisms. First,we design a local attention module combined with deep convolution to enhance local features through context weights while leveraging feature weight sharing to capture high-frequency local information. Second,the Vision Transformer (ViT) self-attention mechanism is used to capture low-frequency global information. Finally,the global and local attention mechanisms are effectively fused,and counting is accomplished through a regression token. The model was tested on the Shanghai Tech Part A,Shanghai Tech Part B,UCF-QNRF,and UCF_CC_50 datasets,achieving MAE values of 64.884,8.958,95.523,and 209.660,and MSE values of 104.411,16.202,173.453,and 282.217,respectively. The results demonstrate that the proposed GLCrowd model exhibits strong performance in crowd counting within dense scenes.